Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study.

Journal: JMIR medical informatics
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Recent advances in artificial intelligence (AI) have contributed to improved predictive modeling in health care, particularly in oncology. Traditional methods often rely on structured tabular data, but these approaches can struggle to capture complex interactions among clinical variables. Image generator for health tabular data (IGHT) transform tabular electronic medical record (EMR) data into structured 2D image matrices, enabling the use of powerful computer vision-based deep learning models. This approach offers a novel baseline for survival prediction in colorectal cancer by leveraging spatial encoding of clinical features, potentially enhancing prognostic accuracy and interpretability.

Authors

  • Seo Hyun Oh
    Department of IT Convergence, Gachon University, 1342, Seongnam-daero, Sung-nam si, Republic of Korea.
  • Youngho Lee
    Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, Republic of Korea. lyh@gachon.ac.kr.
  • Jeong-Heum Baek
    Department of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
  • Woongsang Sunwoo
    Department of Otorhinolaryngology-Head and Neck Surgery, Gachon University College of Medicine, Gil Medical Center, 21, Namdong-daero 774beon-gil, Incheon, Republic of Korea.

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